Structural Neuroimaging Genetics Interactions in Alzheimer’s Disease

Article type: Research Article

Authors: Moon, Seok Woo^{a; 1; *} | Dinov, Ivo D.^{b; d; 1} | Kim, Jaebum^c | Zamanyan, Alen^b | Hobel, Sam^b | Thompson, Paul M.^b | Toga, Arthur W.^b | and for the Alzheimer’s Disease Neuroimaging Initiative

Affiliations: [a] Department of Psychiatry, Konkuk University School of Medicine, Seoul, Republic of Korea | [b] Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA | [c] Department of Animal Biotechnology, Konkuk University, Seoul, Republic of Korea | [d] Department of Human Behavior and Biological Sciences, Statistsics Online Computational Resource (SOCR), Michigan Institute for Data Science (MIDAS), School of Nursing, University of Michigan, Ann Arbor, MI, USA

Correspondence: [*] Correspondence to: Seok Woo Moon, MD, PhD, Dementia center, Department of Neuropsychiatry, Konkuk University Hospital, 82 Gukwon-daero, Chungju-si, Chungbuk-do 380-704, Korea. Tel.: +82 43 840 8990; Fax: +82 43 857 1380; E-mail: [email protected]

Note: [1] These authors have contributed equally to this research.

Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer’s disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferior temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.

Keywords: ADNI, Alzheimer’s disease, GWAS, late-onset, mild cognitive impairment, neuroimaging

DOI: 10.3233/JAD-150335

Journal: Journal of Alzheimer's Disease, vol. 48, no. 4, pp. 1051-1063, 2015